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Designing Effective Flood Early Warning Systems: A Review of Barriers, Best Practices, and Key Characteristics



Details

  • Journal Title:
    Journal of Flood Risk Management
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    Flooding is a disruptive and devastating natural hazard for communities all around the world. To combat the negative effects of flooding, it has become a global priority to implement and research flood early warning systems (FEWS). However, previous research did not comprehensively examine both the technological and social dimensions of FEWS nor the knowledge of their availability and status around the world. To address these gaps, this paper completes a narrative review and synthesizes best practices in FEWS warning and system design, and introduces a “FEWS Around the World” repository which catalogs 3–4 examples per region across six continents. Our analysis shows that while most FEWS include design features outlined in the literature such as timeliness, human capacity, and integration, fewer implement novel components related to impact‐based warnings, participatory science, bottom‐up approaches, advanced technology, and maintaining a preparedness fund, highlighting gaps and opportunities for improvement. Examples demonstrate how these characteristics manifest in diverse contexts, from community‐based systems in Nepal to AI‐driven systems like Google's Flood Hub. By bridging design principles with observed global practices, this paper is designed to aid researchers, practitioners, and communities in people‐centered FEWS development, implementation, and operation, improving resilient flood risk management.
  • Source:
    Journal of Flood Risk Management, 18(4)
  • DOI:
  • ISSN:
    1753-318X ; 1753-318X
  • Format:
    pdf
  • Publisher:
    Wiley
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA23OAR4590137
  • License:
  • Rights Information:
    CC BY-NC-ND
  • Compliance:
    Submitted
  • Main Document Checksum:
    urn:sha-512:7c53bbd80758aaf17637ac0d2bb847aaa4219b3e4c9eb4dfe10c9fcfcd170eb380ac3f551ec903e6a68a9ed72b065f20a823ce783d4e83f350c689d0d9eca2ef
  • Download URL:
  • File Type:
    Filetype[PDF - 836.29 KB ]
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